Break analysis apparatus and method

ABSTRACT

A method and apparatus are disclosed which enable the analysis of a break in a vehicle glazing panel without the attendance of a technician, the method and apparatus utilize capturing an image of the break and processing the image of the break to enable the suitability for repair or replacement of the glazing panel to be determined.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/106,919 filed on Nov. 30, 2020, which is a continuation of U.S.application Ser. No. 16/099,288 filed on Nov. 6, 2018, now U.S. Pat. No.10,852,249, which is the National Stage of International PatentApplication No. PCT/GB2017/051316 filed on May 11, 2017, which claimspriority from British Patent Application No. GB 1608455.0 filed on May13, 2016, all of which are hereby incorporated by reference herein intheir entireties.

BACKGROUND Field

This invention relates generally to an apparatus and a method.Particularly, but not exclusively, the invention relates to an apparatusand a method to be used to analyse breaks in vehicle glazing panels.Further particularly, but not exclusively, the invention relates to amethod and an apparatus to be used to analyse cracks in glass,particularly a vehicle glazing panel.

State of the Art

When driving the presence of debris and other material on the road cancause such material to be deflected into the path of traffic which,when, such material collides with a windscreen, can cause cracks, breaksor other damage to manifest in such windscreens which may necessitatethe need for repair or replacement of the vehicle glazing panel.

For safety reasons and reasons of economy, it is imperative that suchwork is performed as quickly as possible as these cracks can propagatefurther into the windscreen due to the influence of cold weather, say,which can cause the crack to grow. This can cause the crack to changefrom one which needs a minor repair to one which necessitates the needfor a full replacement of the windscreen.

An assessment is required as to whether the damage to a vehicle glazingpanel can be remedied by repair. If the assessment indicates that repairis not feasible then replacement of the glazing panel will be required.

Aspects and embodiments were conceived with the foregoing in mind.

SUMMARY

Viewed from a first aspect, there is provided a break analysis methodfor analysing breaks in a vehicle glazing panel, the method comprising:capturing an image of a break in a vehicle glazing panel; processing theimage of the break.

Viewed from a second aspect, there is provided a break analysisapparatus for analysing breaks in a vehicle glazing panel, the apparatuscomprising: a camera arranged to capture an image of a break in avehicle glazing panel; a processing module operative to process theimage of the break.

Optionally, the apparatus may comprise a mobile computing device whichcomprises the camera. A mobile computing device is an electronic devicewhich is configured to capture images. This may include a mobiletelephone (e.g., a smart phone), a laptop computer, a tablet, a phablet,or a camera. The mobile computing device includes a camera to captureimages.

The mobile computing device may also comprise the processing module.

A method or apparatus in accordance with the first and second aspectsenables an image of a break in a surface to be used to analyse thebreak. This removes the need for physical attendance by a technician toperform any analysis on the crack.

The determination of the need for a replacement glazing panel may bebased on the processing of the image. Thus, the method may include thestep of determining whether or not the glazing panel needs to bereplaced, and/or whether the glazing panel is suitable for repair, basedon the processing of the image.

The image of the break may be captured at an angle inclined relative tothe vehicle glazing panel.

The image may be captured by a mobile computing device held in contactwith the surface of the glazing panel, wherein the mobile computingdevice includes a camera. The mobile computing device may for example bea mobile phone such as a smartphone provided with a camera. Theinvention may be implemented via a software component for processingimage data from the camera in order to determine whether the break canbe repaired or replacement is needed. The software component may bedownloaded to the device such as for example as a stand-alone app or thelike. The software component may comprise an algorithm for making thedetermination and also preferably display user instructions for how toimplement the determination method.

Accordingly according to another aspect the invention comprises acomputer implemented method for determining technical informationregarding a break present in a vehicle glazing panel, wherein the methodincludes the step of downloading a software component to a mobilecomputing device having a camera, the software component arranged toprocess image information captured via the camera in order to determinewhether the break may be repaired or replacement is preferred.

The mobile computing device may be inclined to view the break at apredetermined position in a field of view of the camera. A datumposition may be achieved by ensuring that the computing device isinclined to the panel with an edge contacting the panel.

The predetermined position at which the mobile computing device may beinclined to view the break may be indicated by indicia displayed on thecamera or on the mobile computing device comprising the camera (forexample on a screen).

Additionally or alternatively, the indicia may indicate the centre ofthe field of view (for example on a screen).

Optionally, the camera or mobile computing device may be initially laidflat on the surface of the glazing panel. The mobile computing devicemay then be pivoted or tilted away from the glazing panel, wherein atleast a portion of the mobile computing device remains in contact withthe glazing panel.

Optionally, the camera is positioned in a predetermined positionrelative to the break before the mobile computing device is pivoted. Forexample, the method may include aligning a feature of the mobilecomputing device in a predetermined position relative to the break.

In some embodiments, an edge of the image capture module, such as thetop edge, may be aligned adjacent (or immediately below) the lowestpoint of the break. This means that the initial distance between thecamera and the lowest point of the break can be determined using thegeometry of the mobile computing device.

The image capture module or mobile computing device may then be pivotedaway from the glazing panel, wherein a bottom edge of the mobilecomputing device remains in contact with the glazing panel. The image ofthe break is then captured.

The method may include pivoting or tilting the mobile computing deviceuntil the break is viewable at a predetermined position in a field ofview of the camera.

Optionally, the method includes pivoting the mobile computing deviceuntil the break is in the centre of the field of view of the camera.

The method may include using geometric parameters of the mobilecomputing device and lens parameters of the camera to estimate theparameters of the break. The parameters of the break can include one ormore spatial dimensions indicative of the size of the break.

For example, using the above method the geometric parameters of themobile computing device and the lens parameters of the camera may beused to determine the length of the one or more legs of the break and/orthe size (e.g. width/diameter) of the centre of the break.

Geometric parameters of the mobile computing device may be used todetermine the angle of rotation (or pivot angle) between the mobilecomputing device and the glazing panel.

If the estimated size of the break (e.g. length of one the legs of thebreak) exceeds a given threshold then the method may determine that theglazing panel needs to be replaced. If the estimated size of the break(e.g. length of one the legs of the break) is less than a giventhreshold then the method may determine that the glazing panel issuitable for repair.

The processing of the image may be based upon geometric parameters ofthe mobile computing device to capture the image of the break.

The processing of the image may be further based on chip parameters forthe camera, and/or mobile computing device.

The processing of the image may generate a set of data points which areused to generate a diameter for the break which may be used to determinethe requirement for a replacement vehicle glazing panel.

The method may include outputting a signal or indication indicatingwhether the glazing panel needs to be repaired or replaced.

The break in the glazing panel may comprise a centre and one or morelegs. This break formation is common when a small stone or other objectimpacts a glazing panel. The one or more legs (or cracks) generallyradiate from the centre of the break.

The centre of the break may be substantially circular in shape.

The determination of the need for a replacement or repair of a glazingpanel may comprise generating data indicative of a centre of a break andone or more legs of a break.

The method may include identifying a centre of the break and one or morelegs of the break.

The method may include generating a scaling factor indicating therelative length of the one or more legs of the break compared to thesize of the centre of the break. The size of the centre of the break maybe the diameter, width and/or length of the centre of the break.

The method may include estimating the length of the one or more legs ofthe break (i.e. the absolute length in cm or mm etc.) using the scalingfactor. For example, the length of the one or more legs may be estimatedby multiplying the scaling factor by a predetermined value.

The predetermined value may be an estimate of the actual (i.e. absolute)size of the centre of the break. This provides the advantage that nocalibration object is required, which is at least more convenient forthe user.

Thus, the method of the present invention may comprise determining thescale of the image to estimate the length of the one or more legs of thebreak.

If the estimated length of the one or more legs exceeds a giventhreshold then the method may determine that the glazing panel needs tobe replaced. If the estimated length of the one or more legs is lessthan a given threshold then the method may determine that the break inthe glazing panel is suitable for repair.

The method may comprise outputting a signal indicating that repair ofthe glazing panel is required if the estimated length of the one or morelegs is less than the given threshold.

The method may comprise outputting a signal indicating that replacementof the glazing panel is required if the estimated length of the one ormore legs exceeds the given threshold.

It has been found that the size of the centre of the break generallyvaries less than the length of the one or more legs of the break whencomparing different breaks in glazing panels. As such, the predeterminedvalue may be an average, or mode of the measured sizes of the centre ofa break in a glazing panel.

Optionally, the estimated of the actual width (or diameter) of thecentre of the break (i.e. the predetermined value) may be between 1 mmand 3 mm. A particularly preferred predetermined width (or diameter) ofthe centre of the break may be 2 mm. These ranges/values have beendetermined from studies of breaks carried out by the applicant.

The effect of this is that the estimated size of the centre of thebreak, such as the estimated diameter of the centre of the break, can beused to estimate the length of the legs of the break as thepredetermined value can be used to scale between the relative length ofthe one or more legs relative to the size of the centre of the break andthe estimated actual length of the one or more legs of the break.

For example, if we know that the centre of the break is always going tobe around 2 mm in width (or diameter) and the generated image dataindicates that the legs are twice the length of the diameter of thecentre of the break, then the method may comprise multiplying 2 mm by ascaling factor of 2. This estimates that the legs are 4 mm in length.This helps to build a picture, in the data, of the dimensions of thebreak.

The generated (or estimated) length of the one or more legs may be usedto indicate the estimated size of the break. The size of the break maybe compared to a threshold parameter to determine the need forreplacement of repair of the glazing panel.

If the estimated size of the break exceeds a given threshold then themethod may determine that the glazing panel needs to be replaced. If theestimated size of the break is less than a given threshold then themethod may determine that the glazing panel is suitable for repair.

The comparison may be between the break threshold parameter and thelargest distance across the break.

Optionally, the predetermined estimate of the size of the centre of thebreak may be dependent upon one or more parameters. The parameters maybe input by the user and/or pre-set into the device or processingmodule. For example, the parameters may include: one or more propertiesof the glazing panel (such as type, size, etc.), and/or the speed thevehicle was traveling at when the break occurred.

The processing of the image may comprise filtering the image to remove abackground portion to identify the break.

Morphological refinement may be applied to the image to remove anyclutter from the image and improve the quality of the image data used asthe basis for a determination of whether a replacement glazing panel isrequired.

The method may include cleaning the glazing panel prior to capturing theimage of the break. This may assist in removing any dirt that couldaffect the processing of the image. For example, there is a risk thatdirt could be construed as a break by image processing software.

The method may include disabling a flash function of the image capturingmodule or device before capturing an image of the break. If flashphotography is used, then the light may adversely affect the accuracy ofthe image processing software. For example, the flash may be reflectedin the glazing panel which may affect the identification or analysis ofthe break.

The method may be implemented using computer implemented instructionswhich, when installed into memory, instruct a processor to implement amethod as defined above. A downloadable software component (such as anapp) is preferred.

It will be appreciated that any features of the method may be performedusing the apparatus of the present invention.

These and other aspects of the present invention will be apparent fromand elucidated with reference to, the embodiment described herein.

First and second embodiments of the present invention will now bedescribed, by way of example, and with reference to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a windscreen with a crack.

FIG. 2 illustrates the use of a camera to capture an image of a crack.

FIG. 3 illustrates a processing module which may be used to analyse thecrack in the windscreen of FIG. 1.

FIG. 4 illustrates a flow chart detailing the steps involved inassessing a crack in the windscreen using the system of FIG. 3.

FIG. 5A illustrates an image of a Fourier transform of a crack.

FIG. 5B illustrates the result of a filter applied to the imageillustrated in FIG. 5A.

FIG. 6A illustrates delineated image of a crack.

FIG. 6B illustrates the result of a filter applied to the imageillustrated in FIG. 6A.

FIG. 7 illustrates an arrangement which may be used to model a crack102.

FIG. 8 illustrates the steps involved in generating break parameters.

FIG. 9A schematically illustrates the field of view of a camera with abird's eye view of a windscreen.

FIG. 9B schematically illustrates the field of view of a camera with aninclined view of a windscreen.

FIG. 10 illustrates how a camera may be modelled in order to use theparameters of the camera to analyse the crack.

FIG. 11 illustrates an image which is output from the system todetermine the size of a crack in a windscreen.

DETAILED DESCRIPTION

In a first embodiment, FIG. 1 illustrates a glass windscreen 100 with acrack 102 caused by a stone which had been flicked onto the windscreen.The driver of the car in which the glass windscreen 100 is housed thencaptures an image of the crack 102 using a mobile telephone 104 whichcomprises a camera 106 which is used to capture the image of the crack102. This arrangement is shown from a side perspective in FIG. 2. Thefocal length of the camera 106 is fixed at less than 100 millimetres tocause the focus of the camera to be tight at a small distance.

The image of the crack 102 is then captured, responsive to user input,and mobile telephone 104 is configured to provide a prompt to the userto request that they indicate that they would like the image to betransmitted from the camera 106 to an image processing module 108 whichwe will now describe with reference to FIG. 3. This step enables theuser to assess the quality of the image themselves so that they mayelect to capture another image if they do not consider the image to beclear such as, for example, in inclement weather conditions wherecondensation may be deposited on the lens of camera 106.

The camera 106 converts the captured image to an array of image datausing any suitable method. The camera 106 may save the image data as anexchangeable image file (EXIF) where the lens parameters of the cameraare also stored.

That is to say, the camera 106 is an example of an image capture modulewhich is operative to capture the image of the crack 102 and to transmitthe captured image in the form of captured image data to the imageprocessing module 108 where it may be processed further to extractdetails of the crack.

The image processing module 108 may form part of the mobile telephone104 or it may be geographically distant relative to the mobile telephone104. The image data is transmitted to the image processing module 108 byany suitable means such as, for example, a data bus or the internet.

In a preferred embodiment the image processing module 108 is implementedas a software component downloaded to the mobile phone. This may beimplemented by means of downloading the software component as an app.The software component is capable of processing the image data from thecamera to determine whether the break is susceptible for repair orwhether replacement of the glazing panel may be required as thepreferred solution.

The break analysis module 112 may be a software component downloaded tothe mobile phone preferably as a single download in combination with theimage processing module 108. A single downloaded software component Ipreferably arranged to process the image data from the phone camera andanalyse the break using one or more algorithms implemented in software.

In one implementation, the captured image data is received by the imageprocessing module 108 at data input interface 110. The image data isthen transmitted to the break analysis module 112 which is configured toaccess a routine library 114 where routines may be stored to performoperations on the captured data throughout the analysis of the capturedimage data.

The break analysis module 112 is also configured to access a deviceparameters database 116 where parameters related to the mobile telephone104 are stored.

The parameters related to the mobile telephone 104 include chipparameters which define the image capture performance of the camera 106such as, for example, focal length, and sensor size of the lens, and thedimensional parameters of the mobile telephone 104 such as, for example,the length of the mobile telephone 104 and the distance between the topedge of the mobile telephone 104 and the centre of the image of thecamera 106.

The break analysis module 112 is also operative to interface with adisplay module 118 which is operative to display image data transmittedfrom the break analysis module 112 on a display and to displayparametric data transmitted from the break analysis module 112 on adisplay.

We will now describe, with reference to FIG. 4, the analysis of crack102 using the break analysis module 112.

The break analysis module 112 receives the image data in a step S400.The break analysis module 112 then, in a step S402, calls a Fouriertransform routine from the routine library 114 and uses the Fouriertransform routine to apply a discrete two-dimensional Fourier transformto the image data to produce a transform image as illustrated in FIG.5A.

In FIG. 5A we can see the transformed image. FIG. 5A plots spatialfrequency against the magnitude of the respective Fourier component. Itwill be seen that low spatial frequencies occupy the centre of thetransformed image and higher spatial frequencies can be seen as we moveaway from the centre of the transformed image.

Taking a Fourier Transform of the image enables break analysis module112 to perform analysis of the image in terms of its component spatialfrequencies and phase. As will now be described, it enables the removalof the spatial frequencies in which we have no interest and enables usto reconstruct the image we are interested in by retaining the spatialfrequencies of interest.

A Butterworth bandpass filter is then applied to the transformed imagein a step S404 by break analysis module 112. The mask implemented by theButterworth bandpass filter is illustrated in FIG. 5B. The Butterworthbandpass filter implements a mask on the transformed image shown in FIG.5A and removes the low spatial frequencies (shown by the black dot inthe centre of FIG. 5B and the very high spatial frequencies (the darkborder in the image in FIG. 5B which represents specks of dust and dirton the image.

The Fourier transform of the image data is then inverted in a step S406by calling a Fourier Transform Inversion routine from the routinelibrary 114 to perform an inverse discrete two-dimensional Fouriertransform on the transformed image data.

The performance of the inverse Fourier transform on the transformedimage data transforms the transformed image data from the Fourier domaininto the real domain to generate real domain image data. The resultingreal domain image data is illustrated in FIGS. 6A and 6B.

The use of the Fourier transform to produce the image illustrated inFIG. 6B has the effect of isolating the crack from the background.

The real domain image data is compared to a threshold intensity of 4, ina step S408, to delineate the areas of interest more clearly.

FIG. 6A shows the real domain image data without using a Butterworthbandpass filter. FIG. 6B shows the real domain image data after applyingthe Butterworth bandpass filter to the transformed data illustrated inFIG. 5A and applying thresholding to generate a binary image using athreshold intensity of 4. The Butterworth bandpass filter in thisexample has a rolloff value of 3.

The upper and lower cutoff frequencies of the Butterworth bandpassfilter can be modelled as being linearly dependent on the number ofpixels in the longest side of the image (denoted as m) and can beexpressed respectively as:

${{Freq}_{upper} = {( \frac{m}{4164} )*500}}{{Freq}_{lower} = {( \frac{m}{4164} )*120}}$

This relationship can be altered using standard trials and numericalexperiments.

The image illustrated in FIG. 6B is an image which can include more thanjust the crack 102. It may also include image data which has passedthrough steps S400 to S408 but is due to specks of dirt on thewindscreen and other artefacts of the processing performed by breakanalysis module 112.

The use of the threshold intensity of 4 to generate the binary imageshown in FIG. 6B helps to show the areas of interest more clearly. Theimage shown by the real domain image data illustrated in FIG. 6Bhighlights the crack—including the central crack area—which is an areaof low spatial frequency.

As can be seen the Fourier approach does a very neat job of isolatingthe crack region from the cluttered background assuming that it is infocus and the background is not.

The break analysis module 112 can then call a morphology routine fromthe routines library 114 to remove any clutter from the imageillustrated in FIG. 6B in a step S410.

The morphology routine performs several operations on the imageillustrated in FIG. 6B. This image is a binary image. Regions of blackare zero-valued and regions of white are valued at non-zero. The valueof the pixels is stored in the memory of the break analysis module 112and is the result of the processing in steps S400 to S408.

The first of these operations is a fill-in operation which usesmorphological reconstruction to fill in pixel sized regions of blackthat are surrounded by regions of white with white by replacing the zerovalue with a non-zero value in accordance with the process set out inreference [1].

The second of these operations is a clean-up operation which discardsvery small non-zero valued regions. Very small non-zero valued regionsare defined as non-zero valued regions which occupy an area less thanthe square of (largest dimension of image/500). The largest dimension ofthe image can be determined by the break analysis module simply bycomparing the length of the width of the image with the length of theimage.

The first morphological operation is then repeated to fill in any pixelsized regions of black that are surrounded by regions of white that havebeen generated by the second morphological operation. This is a thirdmorphological operation.

A fourth morphological operation is then performed to join up any legsin the image of the crack 102 which have gaps in them. This isimplemented using a morphological closing operation as described inreference [2]. An erosion is performed followed by a dilation by makinguse of a disk shaped structuring element with a radius of (largestdimension of image\5312) multiplied by 20. The value of 20 has beendetermined empirically and may change. This value can be determinedwithout any undue burden for different image resolutions.

The first morphological operation is then repeated to fill in any pixelsized regions of black that are surrounded by regions of white that havebeen generated by the fourth morphological operation. This is the fifthmorphological operation.

A sixth morphological operation is then performed to discard any smallnon-zero regions. Small regions are defined as regions with an areaequal to the square of (largest dimension of image/100).

A seventh morphological operation is then performed to remove anydisconnected objects in the image. Disconnected objects of interest areobjects that are further away than ¾ of the radius of the largest objectnearest the centre of the image. This means that legs of the crack thatare still disjointed are included but superfluous artefacts areincluded. The seventh morphological operation is implemented by finding,for each remaining region in the image, the centroid, i.e. the centre ofmass of the image, and the length of the major axis of the region. Anadditional weighting is assigned to each region area based on how closethe centroid is to the centre of the image.

The weighting w=1/d² where d is the Euclidean distance between thecentroid and the centre of the image. The largest region, closest to thecentre of the image is selected, and its major axis length is used toset a radius (or ¾ the major axis length from its centroid) outside ofwhich any regions are discarded. That is to say, the morphology routineand the centroid to boundary calculations are configured to retain all“blobs” within a distance from the centre of the crack of the radius ofthe largest object in the image plus half of that radius to ensure thatany discontinuities in the crack 102 are not lost.

The image data, after the morphology has been applied to refine theimage data, can then be used to determine the dimensions of the crack102.

The break analysis module 112 applies further edge detection,morphology, blurring and thresholding to determine the centre of thecrack 102.

It has been observed through experimentation that the centre of a crackis usually around 2 mm in diameter. The break analysis module 112 isoperative to, using the refined image data and the data which resultsfrom the determination of the centre of the crack 102, estimate thelength of the legs of the crack 102 and determine a proportional valuewhich characterises the length of the legs compared to the diameter ofthe centre of the crack 102, that is a scaling factor for the legscompared to the centre of the crack 102. Using the observation that thecentre of the crack is usually 2 mm, the scaling factor can then be usedto determine the length of the legs. This provides uncalibrated analysisof the size of a crack 102.

The determined length of the legs can then be used to approximate thesize of the crack 102 and enables the break analysis module 112 tooutput a determination of whether a replacement windscreen will benecessary or whether repair will be sufficient as it is the size of acrack which is important in making this determination and by comparingthe size of the crack 102 to a repair/replacement threshold the breakanalysis module 112 can automate this determination. The break analysismodule 112 will output this determination to the display module 118.

The output, i.e. whether a replacement windscreen is needed, or not, isthen displayed using display module 118 in a step S412.

The use of an observed estimate of the centre of a crack to estimate thesize of the legs of a crack, which relies on the assumption of a degreeof radial spikeyness in a crack, means that an image can be taken of acrack and used to analyse the crack without any calibration at the sceneto provide a scale for the crack 102.

This method enables an analysis of the crack to be performed in a widerange of conditions and without the attendance of a technician.

In a second embodiment, we now describe how to derive parameters of thecrack 102 using the parameters of the mobile telephone 104 and the lensof the camera 106. This can assist in correcting for any influence thatthe angle has on the image.

The second embodiment can be combined with the first embodiment withoutdeparting from this disclosure.

The arrangement illustrated in FIG. 2 may enable the dimensions of thecrack to be estimated using the chip parameters of the camera 106 andthe geometric parameters of the mobile telephone 104.

In order to calculate the angle of rotation (or pivot or tilt angle) ofthe mobile telephone 104 relative to the windscreen, we can use thegeometric parameters of the mobile telephone 104.

In positioning the crack 102 in the centre of the field of view of thelens of the camera 106 it enables a right angled triangle to be drawn.This is described with respect to FIG. 7.

After the crack 102 has been discovered, the mobile telephone 104 islaid flat on the windscreen with the top edge at the base of the crack102. This means that the distance between the bottom edge of the mobiletelephone 104 and the base of the crack is equal to the length of themobile telephone 104. The mobile telephone 104 is then inclined from thebottom edge of the mobile telephone 104 until the crack 102 is in thecentre of the field of view of camera 106. Indicia may be provided onthe display of mobile telephone 104 to indicate the centre of the fieldof view.

The distance between the bottom edge of the mobile telephone 104 and thelens of the camera 106 can be retrieved from device parameters database116. There is therefore formed a right angled triangle defined by theangle of rotation between the bottom edge of the mobile telephone 104and the windscreen 100, the z-axis of the camera lens and the distanceformed between the bottom edge and the base of the crack.

We now describe how the geometric parameters of the mobile telephone 104and the lens parameters can be used to estimate the parameters of thecrack.

An image of the crack is captured consistently with the processdescribed above in that the mobile telephone 104 is rotated until thecrack 102 is in the centre of the field of view of camera 106.

This enables a right angled triangle to be formed by the z-axis of thecamera lens, the distance formed between the bottom edge and the base ofthe crack and the length between the bottom edge and the camera lens.

With reference to FIG. 8, we describe how the geometry of the mobiletelephone 104 and the lens parameters can be used to estimate theparameters of the break.

In step S800, break analysis module 112 retrieves the distance formedbetween the bottom edge of mobile telephone 104 and the base of thecrack (i.e. the length of mobile telephone 104) and the length betweenthe bottom edge of the mobile telephone 104 and the camera lens from thedevice parameters database 116. The angle of rotation of mobiletelephone 104 can then be calculated in a step S802 using the cosinerelationship between the distance formed between the bottom edge and thebase of the crack and the length between the bottom edge and the cameralens.

We then need to use the cameras parameters to derive plane-planehomographic mapping between the pixels of the camera and the real-worldspatial dimensions of the image. A plane-to-plane homographic mappingroutine is then called from the routines library 114 in a step S804 toderive the real-world spatial dimensions of the image.

The derivation of the homographic map to provide the real-world spatialdimensions of the image is based upon the “pin-hole camera model” wherea camera thinks of the view area of the camera as a rectangular basedcone expanding out relative to the lens of the camera 106. This isillustrated schematically in FIGS. 9A and 9B.

FIG. 9A is for illustration only and illustrates the case where themobile telephone 104 is located directly above the windscreen 100. Thatis to say, the camera 106 provides a bird's eye view of the windscreen100. In this instance the view area A1 is a rectangle and each pixeloccupies the same amount of real-world space (in millimetres).

In this instance, and as illustrated in FIG. 9B, the mobile telephone104 is at an angle relative to the windscreen 100. The angle wascalculated in step S802. The view area A2 then becomes a trapezium whichmeans that pixels close to the camera represent fewer millimetres thanpixels that are further away.

We describe the theoretical basis for how the plane-to-plane homographicmap is derived but it will be understood that this will be implementednumerically using routines that will be available to the break analysismodule 112 using the routines made available using the routines library114.

Consider a rectangular image sensor which forms part of camera 106 and asensor angled from a flat plane by an angle of rotation θ, the areaobserved by the sensor maps to an isosceles trapezium. The width of thebases of this trapezium are directly dependent upon θ. Using theplane-to-plane homographic mapping routine we can use this principle tonumerically estimate the parameters of the crack 102 using the knowledgeof the pixels on the camera 106.

We define a 3D rotation matrix, about the x-axis, as a function of θ,as:

${R_{x}(\theta)} = {\begin{bmatrix}1 & 0 & 0 \\0 & {\cos\theta} & {{- \sin}\theta} \\0 & {\sin\theta} & {\cos\theta}\end{bmatrix}.}$

It will be understood that 0 is the angle of the mobile telephone 104with respect to the windscreen. We can define an origin in Cartesian x,y, and z dimensions at (0,0,0), i.e., the world origin. This is thepoint in the middle of the base-edge of the mobile telephone 104 whichis aligned with the x-axis. The y-axis of this coordinate system is thendirected vertically from the base to the top of the phone. If we assume,for simplicity and without loss of generality, that the camera lies uponthe y-axis, at some distance d_(c) from the base of the phone. Thecamera centre is therefore defined as:

$\begin{matrix}{{\hat{c} = ( {c_{x},c_{y},c_{z}} )^{T}},} \\{= {( {0,d_{c},0} )^{T}.}}\end{matrix}$

The focal length and the vertical and horizontal sensor sizes of thelens of the camera 106 can then be retrieved from device parametersdatabase 116 in a step S806. These parameters may be called chipparameters. This enables us to calculate the area of view from thecamera. The area of view is defined by two quantities which are calledthe horizontal and vertical angle of view (respectively denoted as α_(H)and α_(V)) and they are defined by the following equations:

${\alpha_{H} - \frac{s_{x}}{2f}}{\alpha_{V} = \frac{s_{y}}{2f}}$

where s_(x) and s_(y) are the horizontal and vertical sensor size and fis the focal length.

Having calculated the horizontal and vertical angle of view, breakanalysis module 112 uses the plane-to-plane homographic mapping routineto calculate the edges of the view pyramid to provide us with a field ofview on the windscreen 100 in a step S808. This provides with thetrapezium illustrated schematically in FIG. 9B, i.e., the trapezium thatwe need to correct to compensate for the differing amounts of space thatare occupied by the pixels further from the lens relative to the pixelsnearer to the lens. That is to say, we need to scale the trapezium toensure that the proceeding calculations attribute equal amounts ofreal-world space to each pixel.

This is modelled in the plane-to-plane homographic mapping routine usedby the break analysis module 112 by a line, i.e. a ray, which extendsfrom the lens along the line of view between the lens and the crack 102.This line will intersect the plane represented by the windscreen—thatis, the plane-to-plane homographic mapping routine is modelled as aplane.

In step S810, the plane-to-plane homographic mapping routine calls anumeric solver routine from the routines library 114 to solve thesimultaneous equations which define the plane of the windscreen and theline extending from the lens along the line of view between the lens andthe crack 102. The plane-to-plane homographic mapping routine isprogrammed under the assumption that the plane defining the windscreen100 is flat and the camera 106 is tilted with respect to it. Thisprovides the intersection between the line extending from the lens alongthe line of view and the plane of the windscreen 100.

Theoretically, this can be expressed as the calculation of the raysemanating from the point at the centre of the camera through the cornersof the sensor/image-plane and onto the windscreen which forms theaforementioned trapezium.

We first obtain the intersection of the rays with a plane, parallel tothe image plane, at unit distance, given horizontal and vertical viewingangles of ax and av respectively as defined above.

There are four rays, one for each corner of the rectangular sensor. Theminimum and maximum x values can be defined as:

${x_{\min} = {{- \tan}( \frac{\alpha_{h}}{2} )}}{x_{\max} = {\tan( \frac{\alpha_{h}}{2} )}}$

Similarly, we can define the minimum and maximum y values as:

${y_{\min} = {{- \tan}( \frac{\alpha_{v}}{2} )}}{y_{\max} = {\tan( \frac{\alpha_{v}}{2} )}}$

We can then define the corners of the rectangular sensor as:

x _(tl)=(x _(min) ,y _(min),−1)^(τ)

x _(tr)=(x _(max) ,y _(min),−1)^(τ)

x _(br)=(x _(max) ,y _(max),−1)^(τ)

x _(bl)=(x _(min) ,y _(max),−1)^(τ).

Normalising these coordinates by their magnitude provides us with thedirection of the 4 rays. We define the ray direction for each of thesecoordinates as:

$\begin{matrix}{{x_{i}^{\infty} = \frac{x_{i}}{x_{i}}},} & {i \in {( {{tl},{tr},{br},{bl}} ).}}\end{matrix}$

If we suppose that the phone is rotated, in the x-axis, by θ, we cancalculate that the camera-centre's position is now:

$\begin{matrix}{{\hat{c} = ( {\hat{c_{x}},\hat{c_{y}},\hat{c_{z}}} )^{T}},} \\{= {{R_{x}(\theta)} \cdot ( {0,d_{c},0} )^{T}}}\end{matrix}$

This enables us to define the direction of the rays as:

{circumflex over (x)} _(i) ^(∞) =R _(x)(θ)·x _(i) ^(∞) ,i∈(tl,tr,br,bl).

This gives us the rays in Cartesian coordinates with a known point ofintersection with the plane parallel to the image plane, and we knowthat this intersection occurs only once. This provides a trapeziumindicating the field of view in the real world.

We define the corners of the trapezium as:

V _(i) ,i∈(tl,tr,br,bl)

We calculate the vertices of the trapezium using the line planeintersection formula described in reference [3].

We know that the normal to the windscreen plane is the vector n=(0,0,−1)and that it lies on the world origin which means that the intersectionformula simplifies to:

$\begin{matrix}{{t = \frac{- \hat{c_{z}}}{n \circ {\hat{x}}_{i}^{\infty}}},} \\{v_{i} = \begin{matrix}{{\hat{c} + {t{\hat{x}}_{i}^{\infty}}},} & {i \in {( {{tl},{tr},{br},{bl}} ).}}\end{matrix}}\end{matrix}$

Where the points V_(i), i∈(tl,tr,br,bl) define the vertices of thetrapezium that we need to define the homographic mapping H from theimage-plane to the plane in the real-world using the four-pointcorrespondence technique between the trapezium vertices and the imagecoordinates:

u _(tl)=(0,0)^(τ)

u _(tr)=(h,0)^(τ)

u _(br)=(h,w)^(τ)

u _(bl)=(0,w)^(τ),

where w is the width of the image and h is the height of the image. Thealgorithm which discusses how this homographic map is obtained isdiscussed in reference [4].

The height of the camera above the windscreen can be calculated by thebreak analysis module 112 using the Pythagoras theorem as the distanceformed between the bottom edge of mobile telephone 104 and the base ofthe crack (i.e., the length of mobile telephone 104) and the lengthbetween the bottom edge of the mobile telephone 104 and the camera lenshave been retrieved from the device parameters database 116 in step S800and are still in the memory of the break analysis module 112.

The output from step S810 is the trapezium of view in the real-world(X₁, X₂, X₃, X₄). A comparison between the parameters (X₁, X₂, X₃, X₄)and the corners of the captured image on the windscreen (performed bybreak analysis module 112 in step S812) provide the scaling that isneeded to map the location of the pixels of camera 106 to locations inmillimetres on the field of view on the windscreen 100. This provides uswith the plane-to-plane homographic map. The scaling is in the form of a3×3 matrix which represents scale, rotation, skew, and translationbetween the field of view of the camera and the windscreen 100.

The plane-to-plane homographic map enables the correction of the effectof the perspective on the captured image and the conversion from pixeldimensions to millimetres which enables the break analysis module 112 toderive dimensional parameters for the crack 102.

The plane-to-plane homographic map is a matrix maps the two-dimensionalimage plane of the camera 106 onto a plane representing the windscreen.

The output from the plane-to-plane homographic map provides anorthorectified mask, in millimetres, indicating the location and shapeof the crack.

Responsive to this output from the plane-to-plane homographic map, whichwill, as will be understood, the output from the plane-to-planehomographic mapping routine, the break analysis module 112 calls aconvex hull calculation routine from the routines library 114. Thelocations in millimetres on the field of view of the windscreen areprovided to the convex hull calculation routine from the routineslibrary 114.

A convex hull is, in summary, a space which covers each of the locationsin millimetres on the field of view. The output from the convex hullcalculation routine is data which can be expressed, in simple terms, asa “blob” which will be the same size as the detected crack 102. Thisenables analysis to be performed on the detected crack 102 using theblob.

The break analysis module 112 then calls a smallest-circle routine fromthe routines library 114 which implements a numerical solution to thesmallest circle problem for the convex hull which is output from theconvex hull calculation routine. This module outputs the smallest circlewhich encloses each of the points in the convex hull and thereforeprovides a minimum radius for the crack 102.

The data representing the convex hull, the data representing thesolution to the smallest circle problem for the convex hull and thecalculated radius for the crack are each stored by the break analysismodule 112 in storage which is either local to the processing module 108or remote relative to the processing module 108.

That is to say, break analysis module 112 has used the geometricparameters of the mobile telephone 104 and the parameters of the camera106 to generate a radius for the crack 102.

The parameters and the circle output from the smallest circle routinecan then be displayed using display module 118 in a step S814.

An example image which may be provided by the display module 118 isillustrated in FIG. 11. In this instance the diameter of the smallestcircle is indicated as 16 mm, which means a radius of 8 mm. Theestimated largest crack diameter in this case is 16 mm. The effect hereis that a minimum size for the crack is estimated and can be used todetermine the necessity for a replacement windscreen.

The estimated radius can be compared to a replacement/repair thresholdby the break analysis module 112 to determine whether the crack 102requires replacing or whether repair will be sufficient.

The presence of a case on the mobile telephone 104 is likely tointroduce an error into the measured parameters as this will add to thelength of the mobile telephone 104 but the error is generally around 3%.A 3% error margin is built into the calculations of the break analysismodule 112 and provided on a display by the display module 118.

It is also possible that the distance between the base of the mobiletelephone 104 and the camera 106 will not be available from deviceparameters database 116. In this instance we can estimate the parameterto improve the robustness of the described method.

We can use an inclinometer built into mobile telephone 104 to obtain theangle of the mobile telephone when the image of the crack 102 is beingcaptured. This can be used to calculate the height h using the equation:

h=l*sin(θ)

where l is the length of the mobile telephone 104 and the angle θ is theangle obtained from the inclinometer.

Similarly, the phone angle could be estimated using the angle of view,the image resolution, and the sensor size.

As describe in relation to the first technique, in a preferredembodiment the image processing module 108 is implemented as a softwarecomponent downloaded to the mobile phone. This may be implemented bymeans of downloading the software component as an app. The softwarecomponent is capable of processing the image data from the camera todetermine whether the break is susceptible for repair or whetherreplacement of the glazing panel may be required as the preferredsolution.

The break analysis module 112 may be a software component downloaded tothe mobile phone preferably as a single download in combination with theimage processing module 108. A single downloaded software component Ipreferably arranged to process the image data from the phone camera andanalyse the break using one or more algorithms implemented in software.

The break analysis module 112 is operative to provide an alert ondisplay as to whether a full windscreen replacement is required based onthe radius of the smallest circle. If crack 102 is above a specifiedthreshold, then the break analysis module 112 will indicate the need fora windscreen replacement or not. The alert may be displayed on a displayof mobile telephone 104.

It should be noted that the above-mentioned embodiments illustraterather than limit the invention, and that those skilled in the art willbe capable of designing many alternative embodiments without departingfrom the scope of the invention as defined by the appended claims. Inthe claims, any reference signs placed in parentheses shall not beconstrued as limiting the claims. The word “comprising” and “comprises”,and the like, does not exclude the presence of elements or steps otherthan those listed in any claim or the specification as a whole. In thepresent specification, “comprises” means “includes or consists of” and“comprising” means “including or consisting of”. The singular referenceof an element does not exclude the plural reference of such elements andvice-versa. The invention may be implemented by means of hardwarecomprising several distinct elements, and by means of a suitablyprogrammed computer. In a device claim enumerating several means,several of these means may be embodied by one and the same item ofhardware. The mere fact that certain measures are recited in mutuallydifferent dependent claims does not indicate that a combination of thesemeasures cannot be used to advantage.

REFERENCES

-   [1] http://www.mathworks.com/tagteam/64199_91822y00_eddins_final.pdf-   [2] https://en.wikipedia.org/wiki/closing_(morphology)-   [3] Joseph O'Rourke “Computational Geometry in C”, Cambridge    University Press, 2012-   [4] Richard Hartley and Andrew Zisserman “Multiple View Geometry in    Computer Vision”, Cambridge University Press, 2011.

What is claimed is:
 1. A break analysis method for analysing breaks in avehicle glazing panel, the method comprising: capturing an image of abreak in a vehicle glazing panel using a mobile computing device havinga camera; processing the image of the break; wherein in processing theimage of the break, one or more of: i) geometric parameters of themobile computing device; and, ii) lens parameters of the camera; areused to estimate the size of the break.
 2. A method according to claim1, the method further comprising: determining the need for a replacementor repair of the glazing panel based on the processing of the image. 3.A method according to claim 1, wherein the image of the break iscaptured at an angle inclined relative to the vehicle glazing panel. 4.A method according to claim 1, wherein the image is captured by themobile computing device held in contact with the surface of the glazingpanel; the mobile computing device being inclined to view the break at apredetermined position in a field of view of the camera.
 5. A methodaccording to claim 4, comprising: placing the mobile computing deviceflat on the surface of the glazing panel; and pivoting the mobilecomputing device away from the glazing panel, wherein at least part ofthe mobile computing device remains in contact with the glazing panel;and wherein the camera is positioned in a predetermined positionrelative to the break before the mobile computing device is pivoted. 6.A method according to claim 5, comprising aligning a feature of themobile computing device in a predetermined position relative to thebreak before pivoting the mobile computing device.
 7. A method accordingto claim 1, wherein the geometric parameters of the mobile computingdevice are used to determine the pivot angle between the mobilecomputing device and the glazing panel.
 8. A method according to claim1, wherein the processing of the image is further based on chipparameters for the camera and/or mobile computing device.
 9. A methodaccording to claim 1, wherein the processing of the image generates aset of data points which are used to generate size for the break or azone within the break.
 10. A method according to claim 1, wherein themethod further comprises: determining the requirement for a replacementvehicle glazing panel or a repair of the glazing panel based on theestimated size of the break.
 11. A method according to claim 1,comprising identifying a centre of the break and one or more legs of thebreak.
 12. A method according to claim 11, comprising: generating ascaling factor indicating the relative length of the one or more legs ofthe break compared to the size of the centre of the break; andestimating the length of the one or more legs using the scaling factor.13. A method according to claim 12, wherein estimating the length of theone or more legs of the break comprises multiplying the scaling factorby a predetermined value.
 14. A method according to claim 13, whereinthe predetermined value is a predetermined estimate of the actual sizeof the centre of the break, wherein the size is the diameter, widthand/or length of the centre of the break.
 15. A method according toclaim 14, wherein the estimated length of the one or more legs is usedto indicate the size of the break and the size of the break is comparedto a threshold parameter to determine the need for replacement or repairof the glazing panel.
 16. A method according to claim 15, wherein a stepof determining the need for a replacement or repair of the glazing panelcomprises determining if the estimated length of the one or more legsexceeds a given threshold.
 17. A method according to claim 1, whereinthe processing of the image comprises filtering the image to remove abackground portion to identify the break.
 18. A method according toclaim 17, wherein filtering the image comprises applying morphologicalrefinement to the image.
 19. An apparatus for analysing breaks in avehicle glazing panel, the apparatus comprising: a mobile computingdevice comprising a camera arranged to capture an image of a break in avehicle glazing panel; a processing module operative to process theimage of the break; wherein in processing the image of the break, one ormore of: i) geometric parameters of the mobile computing device; and,ii) lens parameters of the camera; are used to estimate the size of thebreak.
 20. An apparatus according to claim 19, wherein the mobilecomputing device comprises the processing module.